Sökning: "Linear estimation"
Visar resultat 11 - 15 av 501 avhandlingar innehållade orden Linear estimation.
11. Parallel Stochastic Estimation on Multicore Platforms
Sammanfattning : The main part of this thesis concerns parallelization of recursive Bayesian estimation methods, both linear and nonlinear such. Recursive estimation deals with the problem of extracting information about parameters or states of a dynamical system, given noisy measurements of the system output and plays a central role in signal processing, system identification, and automatic control. LÄS MER
12. Estimation Using Low Rank Signal Models
Sammanfattning : Designing estimators based on low rank signal models is a common practice in signal processing. Some of these estimators are designed to use a single low rank snapshot vector, while others employ multiple snapshots. This dissertation deals with both these cases in different contexts. LÄS MER
13. Numerical methods for parameterized linear systems
Sammanfattning : Solving linear systems of equations is a fundamental problem in engineering. Moreover, applications involving the solution to linear systems arise in the social sciences, business, and economics. Specifically, the research conducted in this dissertation explores solutions to linear systems where the system matrix depends nonlinearly on a parameter. LÄS MER
14. Estimation of Speaker Age : Effects of Speech Properties and Speech Material
Sammanfattning : The aim of this thesis was to investigate factors related to accuracy in estimation of speaker age and the role of certain speech properties in perception and manipulation of speaker age, as well as their interaction with the speech material that the age estimates were based on. This thesis consists of three studies. LÄS MER
15. Learning Stochastic Nonlinear Dynamical Systems Using Non-stationary Linear Predictors
Sammanfattning : The estimation problem of stochastic nonlinear parametric models is recognized to be very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the maximum likelihood estimator and the optimal mean-square error predictor using Monte Carlo methods. LÄS MER